Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations4898
Missing cells0
Missing cells (%)0.0%
Duplicate rows772
Duplicate rows (%)15.8%
Total size in memory459.3 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

Dataset has 772 (15.8%) duplicate rowsDuplicates
alcohol is highly overall correlated with chlorides and 1 other fieldsHigh correlation
chlorides is highly overall correlated with alcohol and 1 other fieldsHigh correlation
density is highly overall correlated with alcohol and 3 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
total sulfur dioxide is highly overall correlated with density and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-10-05 05:51:54.317398
Analysis finished2024-10-05 05:52:31.849240
Duration37.53 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8547877
Minimum3.8
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:32.491524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.3
median6.8
Q37.3
95-th percentile8.3
Maximum14.2
Range10.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84386823
Coefficient of variation (CV)0.1231064
Kurtosis2.1721785
Mean6.8547877
Median Absolute Deviation (MAD)0.5
Skewness0.64775147
Sum33574.75
Variance0.71211359
MonotonicityNot monotonic
2024-10-05T08:52:32.804752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 308
 
6.3%
6.6 290
 
5.9%
6.4 280
 
5.7%
6.9 241
 
4.9%
6.7 236
 
4.8%
7 232
 
4.7%
6.5 225
 
4.6%
7.2 206
 
4.2%
7.1 200
 
4.1%
7.4 194
 
4.0%
Other values (58) 2486
50.8%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
0.1%
4.5 1
 
< 0.1%
4.6 1
 
< 0.1%
4.7 5
 
0.1%
4.8 9
 
0.2%
4.9 7
 
0.1%
5 24
0.5%
ValueCountFrequency (%)
14.2 1
 
< 0.1%
11.8 1
 
< 0.1%
10.7 2
 
< 0.1%
10.3 2
 
< 0.1%
10.2 1
 
< 0.1%
10 3
 
0.1%
9.9 2
 
< 0.1%
9.8 8
0.2%
9.7 4
0.1%
9.6 5
0.1%

volatile acidity
Real number (ℝ)

Distinct125
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27824112
Minimum0.08
Maximum1.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:33.220091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.15
Q10.21
median0.26
Q30.32
95-th percentile0.46
Maximum1.1
Range1.02
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.10079455
Coefficient of variation (CV)0.36225612
Kurtosis5.0916258
Mean0.27824112
Median Absolute Deviation (MAD)0.06
Skewness1.5769795
Sum1362.825
Variance0.010159541
MonotonicityNot monotonic
2024-10-05T08:52:33.737920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 263
 
5.4%
0.24 253
 
5.2%
0.26 240
 
4.9%
0.25 231
 
4.7%
0.22 229
 
4.7%
0.27 218
 
4.5%
0.23 216
 
4.4%
0.2 214
 
4.4%
0.3 198
 
4.0%
0.21 191
 
3.9%
Other values (115) 2645
54.0%
ValueCountFrequency (%)
0.08 4
 
0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.1%
0.105 6
 
0.1%
0.11 13
 
0.3%
0.115 3
 
0.1%
0.12 34
0.7%
0.125 3
 
0.1%
0.13 44
0.9%
ValueCountFrequency (%)
1.1 1
< 0.1%
1.005 1
< 0.1%
0.965 1
< 0.1%
0.93 1
< 0.1%
0.91 1
< 0.1%
0.905 1
< 0.1%
0.85 1
< 0.1%
0.815 1
< 0.1%
0.785 1
< 0.1%
0.78 1
< 0.1%

citric acid
Real number (ℝ)

Distinct87
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33419151
Minimum0
Maximum1.66
Zeros19
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:34.071163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.27
median0.32
Q30.39
95-th percentile0.54
Maximum1.66
Range1.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.1210198
Coefficient of variation (CV)0.36212711
Kurtosis6.1749007
Mean0.33419151
Median Absolute Deviation (MAD)0.06
Skewness1.2819204
Sum1636.87
Variance0.014645793
MonotonicityNot monotonic
2024-10-05T08:52:34.370109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 307
 
6.3%
0.28 282
 
5.8%
0.32 257
 
5.2%
0.34 225
 
4.6%
0.29 223
 
4.6%
0.26 219
 
4.5%
0.27 216
 
4.4%
0.49 215
 
4.4%
0.31 200
 
4.1%
0.33 183
 
3.7%
Other values (77) 2571
52.5%
ValueCountFrequency (%)
0 19
0.4%
0.01 7
 
0.1%
0.02 6
 
0.1%
0.03 2
 
< 0.1%
0.04 12
0.2%
0.05 5
 
0.1%
0.06 6
 
0.1%
0.07 12
0.2%
0.08 4
 
0.1%
0.09 12
0.2%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 5
0.1%
0.99 1
 
< 0.1%
0.91 2
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 2
 
< 0.1%
0.8 2
 
< 0.1%

residual sugar
Real number (ℝ)

HIGH CORRELATION 

Distinct310
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3914149
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:34.654382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.7
median5.2
Q39.9
95-th percentile15.7
Maximum65.8
Range65.2
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation5.0720578
Coefficient of variation (CV)0.79357355
Kurtosis3.4698201
Mean6.3914149
Median Absolute Deviation (MAD)3.6
Skewness1.0770938
Sum31305.15
Variance25.72577
MonotonicityNot monotonic
2024-10-05T08:52:34.970007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 187
 
3.8%
1.4 184
 
3.8%
1.6 165
 
3.4%
1.3 147
 
3.0%
1.1 146
 
3.0%
1.5 142
 
2.9%
1.7 99
 
2.0%
1.8 99
 
2.0%
1 93
 
1.9%
2 79
 
1.6%
Other values (300) 3557
72.6%
ValueCountFrequency (%)
0.6 2
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.5%
0.9 39
 
0.8%
0.95 4
 
0.1%
1 93
1.9%
1.05 1
 
< 0.1%
1.1 146
3.0%
1.15 3
 
0.1%
1.2 187
3.8%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
< 0.1%
26.05 2
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 2
< 0.1%
20.8 2
< 0.1%
20.7 2
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045772356
Minimum0.009
Maximum0.346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:35.253129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.027
Q10.036
median0.043
Q30.05
95-th percentile0.067
Maximum0.346
Range0.337
Interquartile range (IQR)0.014

Descriptive statistics

Standard deviation0.021847968
Coefficient of variation (CV)0.47731797
Kurtosis37.5646
Mean0.045772356
Median Absolute Deviation (MAD)0.007
Skewness5.0233307
Sum224.193
Variance0.00047733371
MonotonicityNot monotonic
2024-10-05T08:52:35.537610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 201
 
4.1%
0.036 200
 
4.1%
0.042 184
 
3.8%
0.04 182
 
3.7%
0.046 181
 
3.7%
0.048 174
 
3.6%
0.047 171
 
3.5%
0.045 170
 
3.5%
0.05 170
 
3.5%
0.034 168
 
3.4%
Other values (150) 3097
63.2%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 1
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 4
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 10
0.2%
0.019 9
0.2%
0.02 16
0.3%
ValueCountFrequency (%)
0.346 1
< 0.1%
0.301 1
< 0.1%
0.29 1
< 0.1%
0.271 1
< 0.1%
0.255 1
< 0.1%
0.244 1
< 0.1%
0.24 1
< 0.1%
0.239 1
< 0.1%
0.217 1
< 0.1%
0.212 1
< 0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.308085
Minimum2
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:35.820787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q123
median34
Q346
95-th percentile63
Maximum289
Range287
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.007137
Coefficient of variation (CV)0.48167827
Kurtosis11.466342
Mean35.308085
Median Absolute Deviation (MAD)11
Skewness1.4067449
Sum172939
Variance289.24272
MonotonicityNot monotonic
2024-10-05T08:52:36.136559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 160
 
3.3%
31 132
 
2.7%
35 129
 
2.6%
26 129
 
2.6%
34 128
 
2.6%
36 127
 
2.6%
24 118
 
2.4%
33 112
 
2.3%
28 112
 
2.3%
25 111
 
2.3%
Other values (122) 3640
74.3%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 10
 
0.2%
4 11
 
0.2%
5 25
0.5%
6 32
0.7%
7 25
0.5%
8 35
0.7%
9 29
0.6%
10 55
1.1%
11 45
0.9%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct251
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.36066
Minimum9
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:36.437527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile75
Q1108
median134
Q3167
95-th percentile212
Maximum440
Range431
Interquartile range (IQR)59

Descriptive statistics

Standard deviation42.498065
Coefficient of variation (CV)0.30715425
Kurtosis0.57185323
Mean138.36066
Median Absolute Deviation (MAD)29
Skewness0.39070984
Sum677690.5
Variance1806.0855
MonotonicityNot monotonic
2024-10-05T08:52:36.703140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 69
 
1.4%
113 61
 
1.2%
117 57
 
1.2%
118 55
 
1.1%
114 54
 
1.1%
150 54
 
1.1%
122 54
 
1.1%
128 54
 
1.1%
124 53
 
1.1%
140 52
 
1.1%
Other values (241) 4335
88.5%
ValueCountFrequency (%)
9 1
 
< 0.1%
10 1
 
< 0.1%
18 2
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
24 3
0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
28 4
0.1%
29 2
< 0.1%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
282 1
< 0.1%
272 2
< 0.1%
260 1
< 0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct890
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99402738
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:37.119567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9896385
Q10.9917225
median0.99374
Q30.9961
95-th percentile0.999
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.0043775

Descriptive statistics

Standard deviation0.0029909069
Coefficient of variation (CV)0.0030088778
Kurtosis9.7938069
Mean0.99402738
Median Absolute Deviation (MAD)0.00214
Skewness0.977773
Sum4868.7461
Variance8.9455242 × 10-6
MonotonicityNot monotonic
2024-10-05T08:52:37.436784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992 64
 
1.3%
0.9928 61
 
1.2%
0.9932 53
 
1.1%
0.993 52
 
1.1%
0.9934 50
 
1.0%
0.9938 49
 
1.0%
0.9927 47
 
1.0%
0.9944 46
 
0.9%
0.9948 45
 
0.9%
0.9954 44
 
0.9%
Other values (880) 4387
89.6%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
< 0.1%
ValueCountFrequency (%)
1.03898 1
< 0.1%
1.0103 2
< 0.1%
1.00295 2
< 0.1%
1.00241 1
< 0.1%
1.0024 1
< 0.1%
1.00196 1
< 0.1%
1.00182 1
< 0.1%
1.0017 2
< 0.1%
1.0012 1
< 0.1%
1.00118 1
< 0.1%

pH
Real number (ℝ)

Distinct103
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1882666
Minimum2.72
Maximum3.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:37.736728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.96
Q13.09
median3.18
Q33.28
95-th percentile3.46
Maximum3.82
Range1.1
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.1510006
Coefficient of variation (CV)0.047361346
Kurtosis0.53077495
Mean3.1882666
Median Absolute Deviation (MAD)0.1
Skewness0.45778255
Sum15616.13
Variance0.022801181
MonotonicityNot monotonic
2024-10-05T08:52:38.024466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14 172
 
3.5%
3.16 164
 
3.3%
3.22 146
 
3.0%
3.19 145
 
3.0%
3.18 138
 
2.8%
3.2 137
 
2.8%
3.15 136
 
2.8%
3.08 136
 
2.8%
3.1 135
 
2.8%
3.12 134
 
2.7%
Other values (93) 3455
70.5%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 1
 
< 0.1%
2.77 1
 
< 0.1%
2.79 3
 
0.1%
2.8 3
 
0.1%
2.82 1
 
< 0.1%
2.83 4
0.1%
2.84 1
 
< 0.1%
2.85 9
0.2%
2.86 9
0.2%
ValueCountFrequency (%)
3.82 1
 
< 0.1%
3.81 1
 
< 0.1%
3.8 2
< 0.1%
3.79 1
 
< 0.1%
3.77 2
< 0.1%
3.76 2
< 0.1%
3.75 2
< 0.1%
3.74 2
< 0.1%
3.72 3
0.1%
3.7 1
 
< 0.1%

sulphates
Real number (ℝ)

Distinct79
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48984688
Minimum0.22
Maximum1.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:38.369350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.34
Q10.41
median0.47
Q30.55
95-th percentile0.71
Maximum1.08
Range0.86
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.11412583
Coefficient of variation (CV)0.23298267
Kurtosis1.5909296
Mean0.48984688
Median Absolute Deviation (MAD)0.07
Skewness0.97719368
Sum2399.27
Variance0.013024706
MonotonicityNot monotonic
2024-10-05T08:52:38.919647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 249
 
5.1%
0.46 225
 
4.6%
0.44 216
 
4.4%
0.38 214
 
4.4%
0.42 181
 
3.7%
0.48 179
 
3.7%
0.45 178
 
3.6%
0.47 172
 
3.5%
0.4 168
 
3.4%
0.54 167
 
3.4%
Other values (69) 2949
60.2%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 4
 
0.1%
0.27 13
 
0.3%
0.28 13
 
0.3%
0.29 16
 
0.3%
0.3 31
0.6%
0.31 35
0.7%
0.32 54
1.1%
ValueCountFrequency (%)
1.08 1
 
< 0.1%
1.06 1
 
< 0.1%
1.01 1
 
< 0.1%
1 1
 
< 0.1%
0.99 1
 
< 0.1%
0.98 6
0.1%
0.97 1
 
< 0.1%
0.96 3
0.1%
0.95 5
0.1%
0.94 2
 
< 0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.514267
Minimum8
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:39.303528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.9
Q19.5
median10.4
Q311.4
95-th percentile12.7
Maximum14.2
Range6.2
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.2306206
Coefficient of variation (CV)0.11704292
Kurtosis-0.69842533
Mean10.514267
Median Absolute Deviation (MAD)1
Skewness0.48734199
Sum51498.88
Variance1.514427
MonotonicityNot monotonic
2024-10-05T08:52:39.598697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 229
 
4.7%
9.5 228
 
4.7%
9.2 199
 
4.1%
9 185
 
3.8%
10 162
 
3.3%
10.5 160
 
3.3%
11 158
 
3.2%
10.4 153
 
3.1%
9.1 144
 
2.9%
9.8 136
 
2.8%
Other values (93) 3144
64.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 3
 
0.1%
8.5 9
 
0.2%
8.6 23
 
0.5%
8.7 78
 
1.6%
8.8 107
2.2%
8.9 95
1.9%
9 185
3.8%
9.1 144
2.9%
9.2 199
4.1%
ValueCountFrequency (%)
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 5
 
0.1%
13.9 3
 
0.1%
13.8 2
 
< 0.1%
13.7 7
 
0.1%
13.6 9
0.2%
13.55 1
 
< 0.1%
13.5 12
0.2%
13.4 20
0.4%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8779094
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2024-10-05T08:52:39.819467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88563857
Coefficient of variation (CV)0.15067238
Kurtosis0.21652583
Mean5.8779094
Median Absolute Deviation (MAD)1
Skewness0.1557964
Sum28790
Variance0.78435569
MonotonicityNot monotonic
2024-10-05T08:52:40.019707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2198
44.9%
5 1457
29.7%
7 880
18.0%
8 175
 
3.6%
4 163
 
3.3%
3 20
 
0.4%
9 5
 
0.1%
ValueCountFrequency (%)
3 20
 
0.4%
4 163
 
3.3%
5 1457
29.7%
6 2198
44.9%
7 880
18.0%
8 175
 
3.6%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 175
 
3.6%
7 880
18.0%
6 2198
44.9%
5 1457
29.7%
4 163
 
3.3%
3 20
 
0.4%

Interactions

2024-10-05T08:52:28.516741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:55.109171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:58.143410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:00.581297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:03.760065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:06.226305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:09.380051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:12.412616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:14.811555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:18.382659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:22.497122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:25.443862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:28.722830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:55.425642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:58.330704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:00.824867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:03.946833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:06.593513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:09.647296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:12.609510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:15.008404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:18.694097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:22.915668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:25.782912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:28.923310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:55.744673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:58.518464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:01.211907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:04.143779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:07.053764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:09.844313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:12.792203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:15.196150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:18.966145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:23.291609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:26.007084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:29.123837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:56.172937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:58.730212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:01.614382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:04.361611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:07.329965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:10.062773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:13.009605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:15.411836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:19.308568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:23.660302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:26.206453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:29.342034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:56.534414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:58.947916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:01.894185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:04.577403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:07.543268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:10.279051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:13.202633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:15.612052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:19.736465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:23.860550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:26.424505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:29.558615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:56.747619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:59.159591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:02.123695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:04.805040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:07.762212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:10.501396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:13.416717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:15.841157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:20.147197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:24.060245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:26.643446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:29.777046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:56.961748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:59.363938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:02.377783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:05.004328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:07.979987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:10.712055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:13.627386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:16.056558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:20.531552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:24.274731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:26.860085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:29.956900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:57.144733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:59.543359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:02.592832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:05.196789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:08.159658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:10.896084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:13.809596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:16.379110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:20.862853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:24.481768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:27.044103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:30.213449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:57.348389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:59.748674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:02.804462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:05.393650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:08.434789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:11.092677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:14.009586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:16.641912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:21.198302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:24.674939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:27.240665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:30.440619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:57.543832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:59.936335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:03.141437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:05.593415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:08.711377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:11.306978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:14.208943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:17.087699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:21.630435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:24.874759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:27.441703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:30.640407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:57.738632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:00.168597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:03.327082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:05.809528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:08.909654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:11.494722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:14.392751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:17.536694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:21.873693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:25.057693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:27.704847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:30.849705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:51:57.946028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:00.380656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:03.543379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:06.009977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:09.128109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:12.061720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:14.607628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:17.982959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:22.127536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:25.242933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T08:52:28.164542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-05T08:52:40.202740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidevolatile acidity
alcohol1.000-0.571-0.029-0.822-0.107-0.2730.1490.440-0.445-0.045-0.4770.034
chlorides-0.5711.0000.0330.5080.0950.167-0.054-0.3140.2280.0940.375-0.005
citric acid-0.0290.0331.0000.0910.2980.088-0.1460.0180.0250.0800.093-0.150
density-0.8220.5080.0911.0000.2700.328-0.110-0.3480.7800.0950.5640.010
fixed acidity-0.1070.0950.2980.2701.000-0.025-0.418-0.0840.107-0.0130.113-0.043
free sulfur dioxide-0.2730.1670.0880.328-0.0251.000-0.0060.0240.3460.0520.619-0.081
pH0.149-0.054-0.146-0.110-0.418-0.0061.0000.109-0.1800.140-0.012-0.045
quality0.440-0.3140.018-0.348-0.0840.0240.1091.000-0.0820.033-0.197-0.197
residual sugar-0.4450.2280.0250.7800.1070.346-0.180-0.0821.000-0.0040.4310.109
sulphates-0.0450.0940.0800.095-0.0130.0520.1400.033-0.0041.0000.158-0.017
total sulfur dioxide-0.4770.3750.0930.5640.1130.619-0.012-0.1970.4310.1581.0000.118
volatile acidity0.034-0.005-0.1500.010-0.043-0.081-0.045-0.1970.109-0.0170.1181.000

Missing values

2024-10-05T08:52:31.154448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-05T08:52:31.589968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.00.270.3620.70.04545.0170.01.00103.000.458.86
16.30.300.341.60.04914.0132.00.99403.300.499.56
28.10.280.406.90.05030.097.00.99513.260.4410.16
37.20.230.328.50.05847.0186.00.99563.190.409.96
47.20.230.328.50.05847.0186.00.99563.190.409.96
58.10.280.406.90.05030.097.00.99513.260.4410.16
66.20.320.167.00.04530.0136.00.99493.180.479.66
77.00.270.3620.70.04545.0170.01.00103.000.458.86
86.30.300.341.60.04914.0132.00.99403.300.499.56
98.10.220.431.50.04428.0129.00.99383.220.4511.06
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
48886.80.2200.361.200.05238.0127.00.993303.040.549.25
48894.90.2350.2711.750.03034.0118.00.995403.070.509.46
48906.10.3400.292.200.03625.0100.00.989383.060.4411.86
48915.70.2100.320.900.03838.0121.00.990743.240.4610.66
48926.50.2300.381.300.03229.0112.00.992983.290.549.75
48936.20.2100.291.600.03924.092.00.991143.270.5011.26
48946.60.3200.368.000.04757.0168.00.994903.150.469.65
48956.50.2400.191.200.04130.0111.00.992542.990.469.46
48965.50.2900.301.100.02220.0110.00.988693.340.3812.87
48976.00.2100.380.800.02022.098.00.989413.260.3211.86

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
4237.00.150.2814.70.05129.0149.00.997922.960.399.078
5577.30.190.2713.90.05745.0155.00.998072.940.418.888
3356.80.180.3012.80.06219.0171.00.998083.000.529.077
5897.40.160.3013.70.05633.0168.00.998252.900.448.777
5887.40.160.2715.50.05025.0135.00.998402.900.438.776
5927.40.190.3012.80.05348.5229.00.998603.140.499.176
5937.40.190.3114.50.04539.0193.00.998603.100.509.266
6417.60.200.3014.20.05653.0212.50.999003.140.468.986
285.70.220.2016.00.04441.0113.00.998623.220.468.965
1106.20.230.3617.20.03937.0130.00.999463.230.438.865